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2. When you want to aggregate data and create meaningful segments Even if you manage data on the CRM side, there may be cases where the data is difficult to use or find meaning in. This is also where a CDP comes in handy. For example, let's say that the CRM holds purchase history data (who bought what, when, etc.). Using this data, it is easy to implement a measure for "purchasers of product A" "three days after purchase" using the CRM alone.


On the other hand, what about the following cases? Narrow switzerland telegram database down to customers who have purchased products from product category 1 three or more times in the past year Implement measures that match the average number of days between purchases for customers who make monthly purchases If you want to implement measures like these, you will need to create your own segments separately, such as by linking products with product categories, tallying up purchase frequency, extracting customers who purchase specific products every month, and calculating the average purchase interval.


This cannot be achieved with a CRM system alone, and even if it could, the setting of condition extractions can be complicated. 3. When you want to create segments that incorporate advanced analytics or external data Among CDPs, there are services that are highly compatible with machine learning functions. Using data accumulated in the CDP, it is possible to predict cancellations and implement measures for members who are likely to cancel, or provide incentives according to the likelihood of purchasing.